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schedule (static [,chunk]) The iteration space is divided into blocks of size chunk If chunk is omitted, then the block size is selected to provide one approximately equal sized block per thread The blocks are dealt out to the threads that make up a team in a round robin fashion For example, a chunk size of 2 for 3 threads and 12 iterations will create 6 blocks containing iterations (0,1), (2,3), (4,5), (6,7), (8,9), and (10,11) and assign them to threads as [(0,1), (6,7)] to one thread, [(2,3), (8,9)] to another thread and [(4,5), (10,11)] to the last thread schedule (dynamic [chunk]) The iteration space is divided into blocks of size chunk If chunk is omitted, the block size is set to 1 Each thread is initially given one block of iterations to work with The remaining blocks are placed in a queue When a thread finishes with its current block, it pulls the next block of iterations that need to be computed off the queue This continues until all the iterations have been computed schedule (guided [chunk]) This is a variation of the dynamic schedule optimized to decrease scheduling overhead As with dynamic scheduling, the loop iterations are divided into blocks, and each thread is assigned one block initially and then receives an additional block when it finishes the current one The difference is the size of the blocks: The size of the first block is implementation dependent, but large; block size is decreased rapidly for subsequent blocks, down to the value specified by chunk This method has the benefits of the dynamic schedule, but by starting with large block sizes, the number of scheduling decisions at runtime, and hence the parallel overhead, is greatly reduced schedule (runtime) The runtime schedule stipulates that the actual schedule and chunk size for the loop is to be taken from the value of the environment variable OMP_SCHEDULE This lets a programmer try different schedules without having to recompile for each trial.

image = tkinterPhotoImage(file=image) selftoolbar_imagesappend(image) button = tkinterButton(toolbar, image=image, command=command) buttongrid(row=0, column=len(selftoolbar_images) -1) except tkinterTclError as err: print(err) toolbargrid(row=0, column=0, columnspan=2, sticky=tkinterNW)

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We begin by creating a frame in which all of the window s widgets will be contained Then we create another frame, toolbar, to contain a horizontal row of buttons that have images instead of texts, to serve as toolbar buttons We lay out each toolbar button one after the other in a grid that has one row and as many columns as there are buttons At the end we lay out the toolbar frame itself as the main window frame s rst row, making it north west sticky so that it will always cling to the top left of the window (Tk automatically puts the menu bar above all the widgets laid out in the window) The layout is illustrated in Figure 155, with the menu bar laid out by Tk shown with a white background, and our layouts shown with gray backgrounds

When an image is added to a button it is added as a weak reference, so once the image goes out of scope it is scheduled for garbage collection We must avoid this because we want the buttons to show their images after the initializer has nished, so we create an instance variable, selftoolbar_images, simply to hold references to the images to keep them alive for the program s lifetime Out of the box, Tk can read only a few image le formats, so we have had to use gif images If any image is not found a tkinterTclError exception is raised, so we must be careful to catch this to avoid the program terminating just because of a missing image Notice that we have not made all of the actions available from the menus available as toolbar buttons this is common practice

Consider the loop based program in Fig A13 Because the schedule clause is the same for both Fortran and C, we will only consider the case for C If the runtime associated with different loop iterations changes unpredictably as the program runs, a static schedule is probably not going to be effective We will therefore use the dynamic schedule Scheduling overhead is a serious problem, however, so to minimize the number of scheduling decisions, we will start with a block size of 10 iterations per scheduling decision There are no firm rules, however, and OpenMP programmers usually experiment with a range of schedules and chunk sizes until the optimum values are found For example, parallel loops in programs such as the one in Fig A13 can also be effectively scheduled with a static schedule as long as the chunk size is small enough that work is equally distributed among threads

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